(Introduction to) Current Trends in Visualization
Lecturer: Prof. Tatiana von Landesberger
Course number: 14722.5015 (Bachelor) and 14722.5031 (Master)
Schedule: Following an introductory session at the beginning of the semester, the seminar will take place as a block course at the end of the semester.
Contents
Bachelor:
The seminar “Introduction to Current Trends in Visualization” offers Bachelor's students the opportunity to engage with current research literature and thus gain an initial insight into modern topics and issues in information visualization and visual analytics. The aim is to learn about current developments and develop a basic understanding of how data can be visually processed and interpreted.
The seminar covers various topics, including visual design, interaction with visualizations, and basic evaluation methods. Participants will gain an overview of the use of machine learning and artificial intelligence methods in visualization, in particular large language models (LLMs). Examples will illustrate how visualizations can help to better understand models and classify results.
Topics covered include:
- Use of AI and ML methods in visualization,
- Comparison of model predictions with observational data,
- Basic ideas of uncertainty visualization.
An important part of the seminar is working with scientific literature. Students learn to read technical texts, identify central ideas, and summarize content in an understandable way. The relevant literature is presented in the preliminary discussion and serves as the basis for selecting an individual topic.
Master:
The seminar “Current Trends in Visualization” is aimed at master's students who want to delve deeper into current research questions in information visualization and visual analytics. The focus is on the critical analysis and classification of scientific publications and the discussion of current research trends.
The seminar covers a broad range of topics, from visual design, interaction, and evaluation methods to the close integration of visualization with machine learning and artificial intelligence methods. A particular focus is on the use and integration of large language models (LLMs) in visualization processes, as well as on issues of explainability, trustworthiness, and interpretability of such systems.
Topics covered include:
- Visualization for the analysis and interpretation of ML and AI models (e.g., decision trees, regression analyses, feature importance),
- Comparison and analysis of model predictions and observational data,
- Methods and challenges of uncertainty visualization
The aim of the seminar is to systematically analyze scientific papers, critically evaluate their key contributions, and place them in the context of current research. Students will deepen their ability to structure complex specialist literature, reflect critically on it, and present their findings at an academic level, both in writing and orally. The relevant literature will be presented in the preliminary discussion and will form the basis for individual, thematic in-depth study.